url<-"https://raw.githubusercontent.com/fivethirtyeight/data/master/candy-power-ranking/candy-data.csv"
#candy_file <- read.csv(url)

candy <- read.csv(url, row.names=1)
head(candy,n=5)
##              chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand            1      0       1              0      0                1
## 3 Musketeers         1      0       0              0      1                0
## One dime             0      0       0              0      0                0
## One quarter          0      0       0              0      0                0
## Air Heads            0      1       0              0      0                0
##              hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand       0   1        0        0.732        0.860   66.97173
## 3 Musketeers    0   1        0        0.604        0.511   67.60294
## One dime        0   0        0        0.011        0.116   32.26109
## One quarter     0   0        0        0.011        0.511   46.11650
## Air Heads       0   0        0        0.906        0.511   52.34146

Q1.How many candy types: 85

There are 85 types of candies in total.

nrow(candy)
## [1] 85

Q2: How many fruity candy types?

there are 38 of them.

sum(candy$fruity)
## [1] 38

#2. Favourite candy

candy["Twix", ]$winpercent
## [1] 81.64291

Q3. What is your favorite candy in the dataset and what is it’s winpercent value?

candy["Haribo Happy Cola", ]$winpercent
## [1] 34.15896

My fav is Haribo Happy Cola and its winpercent value is 34%.

Q4. What is the winpercent value for “Kit Kat”?

candy["Kit Kat", ]$winpercent
## [1] 76.7686

The percent is 76.77% for Kit Kat

Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?

candy["Tootsie Roll Snack Bars", ]$winpercent
## [1] 49.6535

The winpercent is 49.65% for Tootsie Roll Snack Bars.

Side-note: the skimr::skim() function

There is a useful skim() function in the skimr package that can help give you a quick overview of a given dataset. Let’s install this package and try it on our candy data.

#install.packages("skimr")
library("skimr")
skim(candy)
Data summary
Name candy
Number of rows 85
Number of columns 12
_______________________
Column type frequency:
numeric 12
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
chocolate 0 1 0.44 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
fruity 0 1 0.45 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
caramel 0 1 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
peanutyalmondy 0 1 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
nougat 0 1 0.08 0.28 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
crispedricewafer 0 1 0.08 0.28 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
hard 0 1 0.18 0.38 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
bar 0 1 0.25 0.43 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
pluribus 0 1 0.52 0.50 0.00 0.00 1.00 1.00 1.00 ▇▁▁▁▇
sugarpercent 0 1 0.48 0.28 0.01 0.22 0.47 0.73 0.99 ▇▇▇▇▆
pricepercent 0 1 0.47 0.29 0.01 0.26 0.47 0.65 0.98 ▇▇▇▇▆
winpercent 0 1 50.32 14.71 22.45 39.14 47.83 59.86 84.18 ▃▇▆▅▂

Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?

Winpercent, it looks on a very different scale.

Q7. What do you think a zero and one represent for the candy$chocolate column?

I think it indicates that the candy$chocolate stands for categorical data

Q8. Plot a histogram of winpercent values

hist(candy$winpercent)

Q9. Is the distribution of winpercent values symmetrical?

The distribution is not symmetrical and is slightly skewed left.

Q10. Is the center of the distribution above or below 50%?

The center of the distribution is below 50%.

Q11. On average is chocolate candy higher or lower ranked than fruit candy?

First, need to find all the chocolate candy rows in the ‘candy’ dataset

inds.choco<-as.logical(candy$chocolate)
candy[inds.choco,]$winpercent
##  [1] 66.97173 67.60294 50.34755 56.91455 38.97504 55.37545 62.28448 56.49050
##  [9] 59.23612 57.21925 76.76860 71.46505 66.57458 55.06407 73.09956 60.80070
## [17] 64.35334 47.82975 54.52645 70.73564 66.47068 69.48379 81.86626 84.18029
## [25] 73.43499 72.88790 65.71629 34.72200 37.88719 76.67378 59.52925 48.98265
## [33] 43.06890 45.73675 49.65350 81.64291 49.52411
chocolate<-candy$winpercent[as.logical(candy$chocolate)]

fruit<-candy$winpercent[as.logical(candy$fruit)]
t.test(chocolate, fruit)
## 
##  Welch Two Sample t-test
## 
## data:  chocolate and fruit
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  11.44563 22.15795
## sample estimates:
## mean of x mean of y 
##  60.92153  44.11974
mean(chocolate)
## [1] 60.92153
mean(fruit)
## [1] 44.11974

Chocolate candies are generally higher ranked than fruity candy.

Q12. Is this difference statistically significant? Yes!

t.test(chocolate, fruit)
## 
##  Welch Two Sample t-test
## 
## data:  chocolate and fruit
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  11.44563 22.15795
## sample estimates:
## mean of x mean of y 
##  60.92153  44.11974

Since the p-value is very small, this means that the difference is significant, and chcolate is more significant than fruity candy.

#3. Overall Candy Rankings Let’s use the base R order() function together with head() to sort the whole dataset by winpercent. Or if you have been getting into the tidyverse and the dplyr package you can use the arrange() function together with head() to do the same thing and answer the following questions:

Q13. What are the five least liked candy types in this set?

“Nik L Nip”, “Boston Baked Beans”, “Chiclets”, “Super Bubble” and “Jawbusters”

head(candy[order(candy$winpercent),], n=5)
##                    chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip                  0      1       0              0      0
## Boston Baked Beans         0      0       0              1      0
## Chiclets                   0      1       0              0      0
## Super Bubble               0      1       0              0      0
## Jawbusters                 0      1       0              0      0
##                    crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip                         0    0   0        1        0.197        0.976
## Boston Baked Beans                0    0   0        1        0.313        0.511
## Chiclets                          0    0   0        1        0.046        0.325
## Super Bubble                      0    0   0        0        0.162        0.116
## Jawbusters                        0    1   0        1        0.093        0.511
##                    winpercent
## Nik L Nip            22.44534
## Boston Baked Beans   23.41782
## Chiclets             24.52499
## Super Bubble         27.30386
## Jawbusters           28.12744

Q14. What are the top 5 all time favorite candy types out of this set?

Kit Kat Snickers Twix ReeseÕs Miniatures ReeseÕs Peanut Butter cup

tail(candy[order(candy$winpercent),], n=5)
##                            chocolate fruity caramel peanutyalmondy nougat
## Snickers                           1      0       1              1      1
## Kit Kat                            1      0       0              0      0
## Twix                               1      0       1              0      0
## ReeseÕs Miniatures                1      0       0              1      0
## ReeseÕs Peanut Butter cup         1      0       0              1      0
##                            crispedricewafer hard bar pluribus sugarpercent
## Snickers                                  0    0   1        0        0.546
## Kit Kat                                   1    0   1        0        0.313
## Twix                                      1    0   1        0        0.546
## ReeseÕs Miniatures                       0    0   0        0        0.034
## ReeseÕs Peanut Butter cup                0    0   0        0        0.720
##                            pricepercent winpercent
## Snickers                          0.651   76.67378
## Kit Kat                           0.511   76.76860
## Twix                              0.906   81.64291
## ReeseÕs Miniatures               0.279   81.86626
## ReeseÕs Peanut Butter cup        0.651   84.18029
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
candy %>% arrange(winpercent) %>% head(5)
##                    chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip                  0      1       0              0      0
## Boston Baked Beans         0      0       0              1      0
## Chiclets                   0      1       0              0      0
## Super Bubble               0      1       0              0      0
## Jawbusters                 0      1       0              0      0
##                    crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip                         0    0   0        1        0.197        0.976
## Boston Baked Beans                0    0   0        1        0.313        0.511
## Chiclets                          0    0   0        1        0.046        0.325
## Super Bubble                      0    0   0        0        0.162        0.116
## Jawbusters                        0    1   0        1        0.093        0.511
##                    winpercent
## Nik L Nip            22.44534
## Boston Baked Beans   23.41782
## Chiclets             24.52499
## Super Bubble         27.30386
## Jawbusters           28.12744

Q15. Make a first barplot of candy ranking based on winpercent values

library(ggplot2)

ggplot(candy) + 
  aes(winpercent, rownames(candy)) +
  geom_col()

Need to improve this plot by reordering the winpercent value

Q16. Use the reorder function to reorder the plot!

ggplot(candy) + 
  aes(winpercent, reorder(rownames(candy),winpercent)) +
  geom_col()

my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"
ggplot(candy) + 
  aes(winpercent, reorder(rownames(candy),winpercent)) +
  geom_col(fill=my_cols)

Q17. What is the worst ranked chocolate candy?

The worst ranked chocolate candy is Sixlets.

Q18. What is the best ranked fruity candy?

Starbust.

#4.Take a look at the pricepercent

#install.packages("ggrepel")
library(ggrepel)


#change red 
my_cols[as.logical(candy$fruity)]="red"
# How about a plot of price vs win
ggplot(candy) +
  aes(winpercent, pricepercent, label=rownames(candy)) +
  geom_point(col=my_cols) + 
  geom_text_repel(col=my_cols, size=2.3, max.overlaps = 7)
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?

Reese_A_miniature is the highest ranked.

Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?

Nik l Nip, Nestie Smarties, Ring pop, Sugar babies, Pop Rocks

#5.Exploring the correlation structure

#install.packages("corrplot")
library(corrplot)
## corrplot 0.90 loaded
cij <- cor(candy)
corrplot(cij)

>Q22. Examining this plot what two variables are anti-correlated (i.e. have minus values)?

Chocolate and fruity variables are anti-correlated

Q23. Similarly, what two variables are most positively correlated?

Chocolate and Bar are very positively correlated HINT: Do you like chocolaty fruity candies?

#6. PCA analysis

pca <- prcomp(candy, scale=TRUE)
summary(pca)
## Importance of components:
##                           PC1    PC2    PC3     PC4    PC5     PC6     PC7
## Standard deviation     2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion  0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
##                            PC8     PC9    PC10    PC11    PC12
## Standard deviation     0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion  0.89998 0.93832 0.97071 0.98683 1.00000
plot(pca$x[,1:2])

plot(pca$x[,1:2], col=my_cols, pch=16)

# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) + 
        aes(x=PC1, y=PC2, 
            size=winpercent/100,  
            text=rownames(my_data),
            label=rownames(my_data)) +
        geom_point(col=my_cols)

p

Again we can use the ggrepel package and the function ggrepel::geom_text_repel() to label up the plot with non overlapping candy names like. We will also add a title and subtitle like so:

library(ggrepel)

p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7)  + 
  theme(legend.position = "none") +
  labs(title="Halloween Candy PCA Space",
       subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
       caption="Data from 538")
## Warning: ggrepel: 41 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

#install.packages("plotly")
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(p)
par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")

>Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you?

Fruity, hard and pluribus are picked up strongly by PC1 in the positive direction. This makes prefect sense.